Deep Reinforcement Learning-Based Optimization for RIS-Based UAV-NOMA Downlink Networks (Invited Paper)

Abstract

This study investigates the application of deep deterministic policy gradient (DDPG) to reconfigurable intelligent surface (RIS)-based unmanned aerial vehicles (UAV)-assisted non-orthogonal multiple access (NOMA) downlink networks. The deployment of UAV equipped with a RIS is important, as the UAV increases the flexibility of the RIS significantly, especially for the case of users who have no line-of-sight (LoS) path to the base station (BS). Therefore, the aim of this study is to maximize the sum-rate by jointly optimizing the power allocation of the BS, the phase shifting of the RIS, and the horizontal position of the UAV. The formulated problem is non-convex, the DDPG algorithm is utilized to solve it. The computer simulation results are provided to show the superior performance of the proposed DDPG-based algorithm.